
#pip install tensorflow

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: tensorflow in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (2.11.0)
Requirement already satisfied: tensorflow-intel==2.11.0 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorflow) (2.11.0)
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Requirement already satisfied: packaging in c:\programdata\anaconda3\lib\site-packages (from tensorflow-intel==2.11.0->tensorflow) (21.3)
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Requirement already satisfied: numpy>=1.20 in c:\programdata\anaconda3\lib\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.21.5)
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Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.51.3)
Requirement already satisfied: libclang>=13.0.0 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorflow-intel==2.11.0->tensorflow) (15.0.6.1)
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Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorflow-intel==2.11.0->tensorflow) (0.31.0)
Requirement already satisfied: keras<2.12,>=2.11.0 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.11.0)
Requirement already satisfied: astunparse>=1.6.0 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorflow-intel==2.11.0->tensorflow) (1.6.3)
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Requirement already satisfied: google-pasta>=0.1.1 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorflow-intel==2.11.0->tensorflow) (0.2.0)
Requirement already satisfied: tensorboard<2.12,>=2.11 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorflow-intel==2.11.0->tensorflow) (2.11.2)
Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\programdata\anaconda3\lib\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.11.0->tensorflow) (0.37.1)
Requirement already satisfied: google-auth<3,>=1.6.3 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.16.2)
Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.8.1)
Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.6.1)
Requirement already satisfied: markdown>=2.6.8 in c:\programdata\anaconda3\lib\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.3.4)
Requirement already satisfied: werkzeug>=1.0.1 in c:\programdata\anaconda3\lib\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.0.3)
Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.4.6)
Requirement already satisfied: requests<3,>=2.21.0 in c:\programdata\anaconda3\lib\site-packages (from tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2.28.1)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in c:\programdata\anaconda3\lib\site-packages (from packaging->tensorflow-intel==2.11.0->tensorflow) (3.0.9)
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Requirement already satisfied: rsa<5,>=3.1.4 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (4.9)
Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\programdata\anaconda3\lib\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.2.8)
Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.3.1)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\programdata\anaconda3\lib\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (1.26.11)
Requirement already satisfied: certifi>=2017.4.17 in c:\programdata\anaconda3\lib\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (2022.9.14)
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Requirement already satisfied: idna<4,>=2.5 in c:\programdata\anaconda3\lib\site-packages (from requests<3,>=2.21.0->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.3)
Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in c:\programdata\anaconda3\lib\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (0.4.8)
Requirement already satisfied: oauthlib>=3.0.0 in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<2.12,>=2.11->tensorflow-intel==2.11.0->tensorflow) (3.2.2)
Note: you may need to restart the kernel to use updated packages.


#pip install keras

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: keras in c:\users\navyakala sv\appdata\roaming\python\python39\site-packages (2.11.0)
Note: you may need to restart the kernel to use updated packages.


import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras import layers, Model
from sklearn.model_selection import train_test_split
import numpy as np
from keras import Model
from keras.layers import Conv2D, PReLU,BatchNormalization, Flatten
from keras.layers import UpSampling2D, LeakyReLU, Dense, Input, add
from tqdm import tqdm


#Define blocks to build the generator
def res_block(ip):
    
    res_model = Conv2D(64, (3,3), padding = "same")(ip)
    res_model = BatchNormalization(momentum = 0.5)(res_model)
    res_model = PReLU(shared_axes = [1,2])(res_model)
    
    res_model = Conv2D(64, (3,3), padding = "same")(res_model)
    res_model = BatchNormalization(momentum = 0.5)(res_model)
    
    return add([ip,res_model])

def upscale_block(ip):
    
    up_model = Conv2D(256, (3,3), padding="same")(ip)
    up_model = UpSampling2D( size = 2 )(up_model)
    up_model = PReLU(shared_axes=[1,2])(up_model)
    
    return up_model


#Generator model
def create_gen(gen_ip, num_res_block):
    layers = Conv2D(64, (9,9), padding="same")(gen_ip)
    layers = PReLU(shared_axes=[1,2])(layers)

    temp = layers

    for i in range(num_res_block):
        layers = res_block(layers)

    layers = Conv2D(64, (3,3), padding="same")(layers)
    layers = BatchNormalization(momentum=0.5)(layers)
    layers = add([layers,temp])

    layers = upscale_block(layers)
    layers = upscale_block(layers)

    op = Conv2D(3, (9,9), padding="same")(layers)

    return Model(inputs=gen_ip, outputs=op)


#Descriminator block that will be used to construct the discriminator
def discriminator_block(ip, filters, strides=1, bn=True):
    
    disc_model = Conv2D(filters, (3,3), strides = strides, padding="same")(ip)
    
    if bn:
        disc_model = BatchNormalization( momentum=0.8 )(disc_model)
    
    disc_model = LeakyReLU( alpha=0.2 )(disc_model)
    
    return disc_model


#Descriminartor
def create_disc(disc_ip):

    df = 64
    
    d1 = discriminator_block(disc_ip, df, bn=False)
    d2 = discriminator_block(d1, df, strides=2)
    d3 = discriminator_block(d2, df*2)
    d4 = discriminator_block(d3, df*2, strides=2)
    d5 = discriminator_block(d4, df*4)
    d6 = discriminator_block(d5, df*4, strides=2)
    d7 = discriminator_block(d6, df*8)
    d8 = discriminator_block(d7, df*8, strides=2)
    
    d8_5 = Flatten()(d8)
    d9 = Dense(df*16)(d8_5)
    d10 = LeakyReLU(alpha=0.2)(d9)
    validity = Dense(1, activation='sigmoid')(d10)

    return Model(disc_ip, validity)


#VGG19 
#We need VGG19 for the feature map obtained by the j-th convolution (after activation) 
#before the i-th maxpooling layer within the VGG19 network.(as described in the paper)
#Let us pick the 3rd block, last conv layer. 
#Build a pre-trained VGG19 model that outputs image features extracted at the
# third block of the model
# VGG architecture: https://github.com/keras-team/keras/blob/master/keras/applications/vgg19.py
from keras.applications import VGG19

def build_vgg(hr_shape):
    
    vgg = VGG19(weights="imagenet",include_top=False, input_shape=hr_shape)
    
    return Model(inputs=vgg.inputs, outputs=vgg.layers[10].output)


#Combined model
def create_comb(gen_model, disc_model, vgg, lr_ip, hr_ip):
    gen_img = gen_model(lr_ip)
    
    gen_features = vgg(gen_img)
    
    disc_model.trainable = False
    validity = disc_model(gen_img)
    
    return Model(inputs=[lr_ip, hr_ip], outputs=[validity, gen_features])


import cv2
import numpy as np
import os
import matplotlib.pyplot as plt

train_dir = r"HR1/" 

for img in os.listdir(train_dir+"/HR/"):
    img_array = cv2.imread(train_dir +"/HR/"+ img)
    
    img_array = cv2.resize(img_array,(32,32))
    cv2.imwrite("HR1/lr1/" + img, img_array)


from skimage import io
n=100
path1=r"HR1/lr1/"
path2=r"HR1/HR/"
lr_list = os.listdir(path1)[:n]

lr_images = []
for img in lr_list:
    img_lr = cv2.imread(path1+img)
    img_lr = cv2.cvtColor(img_lr, cv2.COLOR_BGR2RGB)
    lr_images.append(img_lr)   


hr_list = os.listdir(path2)[:n]
   
hr_images = []
for img in hr_list:
    img_hr = cv2.imread(path2 + img)
    img_hr = cv2.cvtColor(img_hr, cv2.COLOR_BGR2RGB)
    hr_images.append(img_hr)   

lr_images = np.array(lr_images)
hr_images = np.array(hr_images)


import random
import numpy as np
image_number = random.randint(0, len(lr_images)-1)
plt.figure(figsize=(6, 6))
plt.subplot(121)
plt.imshow(np.reshape(lr_images[image_number], (32, 32, 3)))
plt.subplot(122)
plt.imshow(np.reshape(hr_images[image_number], (128, 128, 3)))
plt.show()


#Scale values
lr_images = lr_images / 255.
hr_images = hr_images / 255.

#Split to train and test
lr_train, lr_test, hr_train, hr_test = train_test_split(lr_images, hr_images,test_size=0.33, random_state=42)


hr_shape = (hr_train.shape[1], hr_train.shape[2], hr_train.shape[3])
lr_shape = (lr_train.shape[1], lr_train.shape[2], lr_train.shape[3])

lr_ip = Input(shape=lr_shape)
hr_ip = Input(shape=hr_shape)


generator = create_gen(lr_ip, num_res_block = 16)
generator.summary()

Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 32, 32, 3)]  0           []                               
                                                                                                  
 conv2d (Conv2D)                (None, 32, 32, 64)   15616       ['input_1[0][0]']                
                                                                                                  
 p_re_lu (PReLU)                (None, 32, 32, 64)   64          ['conv2d[0][0]']                 
                                                                                                  
 conv2d_1 (Conv2D)              (None, 32, 32, 64)   36928       ['p_re_lu[0][0]']                
                                                                                                  
 batch_normalization (BatchNorm  (None, 32, 32, 64)  256         ['conv2d_1[0][0]']               
 alization)                                                                                       
                                                                                                  
 p_re_lu_1 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization[0][0]']    
                                                                                                  
 conv2d_2 (Conv2D)              (None, 32, 32, 64)   36928       ['p_re_lu_1[0][0]']              
                                                                                                  
 batch_normalization_1 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_2[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 add (Add)                      (None, 32, 32, 64)   0           ['p_re_lu[0][0]',                
                                                                  'batch_normalization_1[0][0]']  
                                                                                                  
 conv2d_3 (Conv2D)              (None, 32, 32, 64)   36928       ['add[0][0]']                    
                                                                                                  
 batch_normalization_2 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_3[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 p_re_lu_2 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization_2[0][0]']  
                                                                                                  
 conv2d_4 (Conv2D)              (None, 32, 32, 64)   36928       ['p_re_lu_2[0][0]']              
                                                                                                  
 batch_normalization_3 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_4[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 add_1 (Add)                    (None, 32, 32, 64)   0           ['add[0][0]',                    
                                                                  'batch_normalization_3[0][0]']  
                                                                                                  
 conv2d_5 (Conv2D)              (None, 32, 32, 64)   36928       ['add_1[0][0]']                  
                                                                                                  
 batch_normalization_4 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_5[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 p_re_lu_3 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization_4[0][0]']  
                                                                                                  
 conv2d_6 (Conv2D)              (None, 32, 32, 64)   36928       ['p_re_lu_3[0][0]']              
                                                                                                  
 batch_normalization_5 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_6[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 add_2 (Add)                    (None, 32, 32, 64)   0           ['add_1[0][0]',                  
                                                                  'batch_normalization_5[0][0]']  
                                                                                                  
 conv2d_7 (Conv2D)              (None, 32, 32, 64)   36928       ['add_2[0][0]']                  
                                                                                                  
 batch_normalization_6 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_7[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 p_re_lu_4 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization_6[0][0]']  
                                                                                                  
 conv2d_8 (Conv2D)              (None, 32, 32, 64)   36928       ['p_re_lu_4[0][0]']              
                                                                                                  
 batch_normalization_7 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_8[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 add_3 (Add)                    (None, 32, 32, 64)   0           ['add_2[0][0]',                  
                                                                  'batch_normalization_7[0][0]']  
                                                                                                  
 conv2d_9 (Conv2D)              (None, 32, 32, 64)   36928       ['add_3[0][0]']                  
                                                                                                  
 batch_normalization_8 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_9[0][0]']               
 rmalization)                                                                                     
                                                                                                  
 p_re_lu_5 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization_8[0][0]']  
                                                                                                  
 conv2d_10 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_5[0][0]']              
                                                                                                  
 batch_normalization_9 (BatchNo  (None, 32, 32, 64)  256         ['conv2d_10[0][0]']              
 rmalization)                                                                                     
                                                                                                  
 add_4 (Add)                    (None, 32, 32, 64)   0           ['add_3[0][0]',                  
                                                                  'batch_normalization_9[0][0]']  
                                                                                                  
 conv2d_11 (Conv2D)             (None, 32, 32, 64)   36928       ['add_4[0][0]']                  
                                                                                                  
 batch_normalization_10 (BatchN  (None, 32, 32, 64)  256         ['conv2d_11[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_6 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization_10[0][0]'] 
                                                                                                  
 conv2d_12 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_6[0][0]']              
                                                                                                  
 batch_normalization_11 (BatchN  (None, 32, 32, 64)  256         ['conv2d_12[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_5 (Add)                    (None, 32, 32, 64)   0           ['add_4[0][0]',                  
                                                                  'batch_normalization_11[0][0]'] 
                                                                                                  
 conv2d_13 (Conv2D)             (None, 32, 32, 64)   36928       ['add_5[0][0]']                  
                                                                                                  
 batch_normalization_12 (BatchN  (None, 32, 32, 64)  256         ['conv2d_13[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_7 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization_12[0][0]'] 
                                                                                                  
 conv2d_14 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_7[0][0]']              
                                                                                                  
 batch_normalization_13 (BatchN  (None, 32, 32, 64)  256         ['conv2d_14[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_6 (Add)                    (None, 32, 32, 64)   0           ['add_5[0][0]',                  
                                                                  'batch_normalization_13[0][0]'] 
                                                                                                  
 conv2d_15 (Conv2D)             (None, 32, 32, 64)   36928       ['add_6[0][0]']                  
                                                                                                  
 batch_normalization_14 (BatchN  (None, 32, 32, 64)  256         ['conv2d_15[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_8 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization_14[0][0]'] 
                                                                                                  
 conv2d_16 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_8[0][0]']              
                                                                                                  
 batch_normalization_15 (BatchN  (None, 32, 32, 64)  256         ['conv2d_16[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_7 (Add)                    (None, 32, 32, 64)   0           ['add_6[0][0]',                  
                                                                  'batch_normalization_15[0][0]'] 
                                                                                                  
 conv2d_17 (Conv2D)             (None, 32, 32, 64)   36928       ['add_7[0][0]']                  
                                                                                                  
 batch_normalization_16 (BatchN  (None, 32, 32, 64)  256         ['conv2d_17[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_9 (PReLU)              (None, 32, 32, 64)   64          ['batch_normalization_16[0][0]'] 
                                                                                                  
 conv2d_18 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_9[0][0]']              
                                                                                                  
 batch_normalization_17 (BatchN  (None, 32, 32, 64)  256         ['conv2d_18[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_8 (Add)                    (None, 32, 32, 64)   0           ['add_7[0][0]',                  
                                                                  'batch_normalization_17[0][0]'] 
                                                                                                  
 conv2d_19 (Conv2D)             (None, 32, 32, 64)   36928       ['add_8[0][0]']                  
                                                                                                  
 batch_normalization_18 (BatchN  (None, 32, 32, 64)  256         ['conv2d_19[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_10 (PReLU)             (None, 32, 32, 64)   64          ['batch_normalization_18[0][0]'] 
                                                                                                  
 conv2d_20 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_10[0][0]']             
                                                                                                  
 batch_normalization_19 (BatchN  (None, 32, 32, 64)  256         ['conv2d_20[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_9 (Add)                    (None, 32, 32, 64)   0           ['add_8[0][0]',                  
                                                                  'batch_normalization_19[0][0]'] 
                                                                                                  
 conv2d_21 (Conv2D)             (None, 32, 32, 64)   36928       ['add_9[0][0]']                  
                                                                                                  
 batch_normalization_20 (BatchN  (None, 32, 32, 64)  256         ['conv2d_21[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_11 (PReLU)             (None, 32, 32, 64)   64          ['batch_normalization_20[0][0]'] 
                                                                                                  
 conv2d_22 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_11[0][0]']             
                                                                                                  
 batch_normalization_21 (BatchN  (None, 32, 32, 64)  256         ['conv2d_22[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_10 (Add)                   (None, 32, 32, 64)   0           ['add_9[0][0]',                  
                                                                  'batch_normalization_21[0][0]'] 
                                                                                                  
 conv2d_23 (Conv2D)             (None, 32, 32, 64)   36928       ['add_10[0][0]']                 
                                                                                                  
 batch_normalization_22 (BatchN  (None, 32, 32, 64)  256         ['conv2d_23[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_12 (PReLU)             (None, 32, 32, 64)   64          ['batch_normalization_22[0][0]'] 
                                                                                                  
 conv2d_24 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_12[0][0]']             
                                                                                                  
 batch_normalization_23 (BatchN  (None, 32, 32, 64)  256         ['conv2d_24[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_11 (Add)                   (None, 32, 32, 64)   0           ['add_10[0][0]',                 
                                                                  'batch_normalization_23[0][0]'] 
                                                                                                  
 conv2d_25 (Conv2D)             (None, 32, 32, 64)   36928       ['add_11[0][0]']                 
                                                                                                  
 batch_normalization_24 (BatchN  (None, 32, 32, 64)  256         ['conv2d_25[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_13 (PReLU)             (None, 32, 32, 64)   64          ['batch_normalization_24[0][0]'] 
                                                                                                  
 conv2d_26 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_13[0][0]']             
                                                                                                  
 batch_normalization_25 (BatchN  (None, 32, 32, 64)  256         ['conv2d_26[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_12 (Add)                   (None, 32, 32, 64)   0           ['add_11[0][0]',                 
                                                                  'batch_normalization_25[0][0]'] 
                                                                                                  
 conv2d_27 (Conv2D)             (None, 32, 32, 64)   36928       ['add_12[0][0]']                 
                                                                                                  
 batch_normalization_26 (BatchN  (None, 32, 32, 64)  256         ['conv2d_27[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_14 (PReLU)             (None, 32, 32, 64)   64          ['batch_normalization_26[0][0]'] 
                                                                                                  
 conv2d_28 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_14[0][0]']             
                                                                                                  
 batch_normalization_27 (BatchN  (None, 32, 32, 64)  256         ['conv2d_28[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_13 (Add)                   (None, 32, 32, 64)   0           ['add_12[0][0]',                 
                                                                  'batch_normalization_27[0][0]'] 
                                                                                                  
 conv2d_29 (Conv2D)             (None, 32, 32, 64)   36928       ['add_13[0][0]']                 
                                                                                                  
 batch_normalization_28 (BatchN  (None, 32, 32, 64)  256         ['conv2d_29[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_15 (PReLU)             (None, 32, 32, 64)   64          ['batch_normalization_28[0][0]'] 
                                                                                                  
 conv2d_30 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_15[0][0]']             
                                                                                                  
 batch_normalization_29 (BatchN  (None, 32, 32, 64)  256         ['conv2d_30[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_14 (Add)                   (None, 32, 32, 64)   0           ['add_13[0][0]',                 
                                                                  'batch_normalization_29[0][0]'] 
                                                                                                  
 conv2d_31 (Conv2D)             (None, 32, 32, 64)   36928       ['add_14[0][0]']                 
                                                                                                  
 batch_normalization_30 (BatchN  (None, 32, 32, 64)  256         ['conv2d_31[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 p_re_lu_16 (PReLU)             (None, 32, 32, 64)   64          ['batch_normalization_30[0][0]'] 
                                                                                                  
 conv2d_32 (Conv2D)             (None, 32, 32, 64)   36928       ['p_re_lu_16[0][0]']             
                                                                                                  
 batch_normalization_31 (BatchN  (None, 32, 32, 64)  256         ['conv2d_32[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_15 (Add)                   (None, 32, 32, 64)   0           ['add_14[0][0]',                 
                                                                  'batch_normalization_31[0][0]'] 
                                                                                                  
 conv2d_33 (Conv2D)             (None, 32, 32, 64)   36928       ['add_15[0][0]']                 
                                                                                                  
 batch_normalization_32 (BatchN  (None, 32, 32, 64)  256         ['conv2d_33[0][0]']              
 ormalization)                                                                                    
                                                                                                  
 add_16 (Add)                   (None, 32, 32, 64)   0           ['batch_normalization_32[0][0]', 
                                                                  'p_re_lu[0][0]']                
                                                                                                  
 conv2d_34 (Conv2D)             (None, 32, 32, 256)  147712      ['add_16[0][0]']                 
                                                                                                  
 up_sampling2d (UpSampling2D)   (None, 64, 64, 256)  0           ['conv2d_34[0][0]']              
                                                                                                  
 p_re_lu_17 (PReLU)             (None, 64, 64, 256)  256         ['up_sampling2d[0][0]']          
                                                                                                  
 conv2d_35 (Conv2D)             (None, 64, 64, 256)  590080      ['p_re_lu_17[0][0]']             
                                                                                                  
 up_sampling2d_1 (UpSampling2D)  (None, 128, 128, 25  0          ['conv2d_35[0][0]']              
                                6)                                                                
                                                                                                  
 p_re_lu_18 (PReLU)             (None, 128, 128, 25  256         ['up_sampling2d_1[0][0]']        
                                6)                                                                
                                                                                                  
 conv2d_36 (Conv2D)             (None, 128, 128, 3)  62211       ['p_re_lu_18[0][0]']             
                                                                                                  
==================================================================================================
Total params: 2,044,291
Trainable params: 2,040,067
Non-trainable params: 4,224
__________________________________________________________________________________________________


discriminator = create_disc(hr_ip)
discriminator.compile(loss="binary_crossentropy", optimizer="adam", metrics=['accuracy'])
discriminator.summary()

Model: "model_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_2 (InputLayer)        [(None, 128, 128, 3)]     0         
                                                                 
 conv2d_37 (Conv2D)          (None, 128, 128, 64)      1792      
                                                                 
 leaky_re_lu (LeakyReLU)     (None, 128, 128, 64)      0         
                                                                 
 conv2d_38 (Conv2D)          (None, 64, 64, 64)        36928     
                                                                 
 batch_normalization_33 (Bat  (None, 64, 64, 64)       256       
 chNormalization)                                                
                                                                 
 leaky_re_lu_1 (LeakyReLU)   (None, 64, 64, 64)        0         
                                                                 
 conv2d_39 (Conv2D)          (None, 64, 64, 128)       73856     
                                                                 
 batch_normalization_34 (Bat  (None, 64, 64, 128)      512       
 chNormalization)                                                
                                                                 
 leaky_re_lu_2 (LeakyReLU)   (None, 64, 64, 128)       0         
                                                                 
 conv2d_40 (Conv2D)          (None, 32, 32, 128)       147584    
                                                                 
 batch_normalization_35 (Bat  (None, 32, 32, 128)      512       
 chNormalization)                                                
                                                                 
 leaky_re_lu_3 (LeakyReLU)   (None, 32, 32, 128)       0         
                                                                 
 conv2d_41 (Conv2D)          (None, 32, 32, 256)       295168    
                                                                 
 batch_normalization_36 (Bat  (None, 32, 32, 256)      1024      
 chNormalization)                                                
                                                                 
 leaky_re_lu_4 (LeakyReLU)   (None, 32, 32, 256)       0         
                                                                 
 conv2d_42 (Conv2D)          (None, 16, 16, 256)       590080    
                                                                 
 batch_normalization_37 (Bat  (None, 16, 16, 256)      1024      
 chNormalization)                                                
                                                                 
 leaky_re_lu_5 (LeakyReLU)   (None, 16, 16, 256)       0         
                                                                 
 conv2d_43 (Conv2D)          (None, 16, 16, 512)       1180160   
                                                                 
 batch_normalization_38 (Bat  (None, 16, 16, 512)      2048      
 chNormalization)                                                
                                                                 
 leaky_re_lu_6 (LeakyReLU)   (None, 16, 16, 512)       0         
                                                                 
 conv2d_44 (Conv2D)          (None, 8, 8, 512)         2359808   
                                                                 
 batch_normalization_39 (Bat  (None, 8, 8, 512)        2048      
 chNormalization)                                                
                                                                 
 leaky_re_lu_7 (LeakyReLU)   (None, 8, 8, 512)         0         
                                                                 
 flatten (Flatten)           (None, 32768)             0         
                                                                 
 dense (Dense)               (None, 1024)              33555456  
                                                                 
 leaky_re_lu_8 (LeakyReLU)   (None, 1024)              0         
                                                                 
 dense_1 (Dense)             (None, 1)                 1025      
                                                                 
=================================================================
Total params: 38,249,281
Trainable params: 38,245,569
Non-trainable params: 3,712
_________________________________________________________________


vgg = build_vgg((128,128,3))
print(vgg.summary())
vgg.trainable = False

Model: "model_2"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_3 (InputLayer)        [(None, 128, 128, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 128, 128, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 128, 128, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 64, 64, 64)        0         
                                                                 
 block2_conv1 (Conv2D)       (None, 64, 64, 128)       73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 64, 64, 128)       147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 32, 32, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 32, 32, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 32, 32, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 32, 32, 256)       590080    
                                                                 
 block3_conv4 (Conv2D)       (None, 32, 32, 256)       590080    
                                                                 
=================================================================
Total params: 2,325,568
Trainable params: 2,325,568
Non-trainable params: 0
_________________________________________________________________
None


gan_model = create_comb(generator, discriminator, vgg, lr_ip, hr_ip)


gan_model.compile(loss=["binary_crossentropy", "mse"], loss_weights=[1e-3, 1], optimizer="adam")
gan_model.summary()

Model: "model_3"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 32, 32, 3)]  0           []                               
                                                                                                  
 model (Functional)             (None, 128, 128, 3)  2044291     ['input_1[0][0]']                
                                                                                                  
 input_2 (InputLayer)           [(None, 128, 128, 3  0           []                               
                                )]                                                                
                                                                                                  
 model_1 (Functional)           (None, 1)            38249281    ['model[0][0]']                  
                                                                                                  
 model_2 (Functional)           (None, 32, 32, 256)  2325568     ['model[0][0]']                  
                                                                                                  
==================================================================================================
Total params: 42,619,140
Trainable params: 2,040,067
Non-trainable params: 40,579,073
__________________________________________________________________________________________________


batch_size = 1  
train_lr_batches = []
train_hr_batches = []
for it in range(int(hr_train.shape[0] / batch_size)):
    start_idx = it * batch_size
    end_idx = start_idx + batch_size
    train_hr_batches.append(hr_train[start_idx:end_idx])
    train_lr_batches.append(lr_train[start_idx:end_idx])


epochs = 200
#Enumerate training over epochs
for e in range(epochs):
    
    fake_label = np.zeros((batch_size, 1)) # Assign a label of 0 to all fake (generated images)
    real_label = np.ones((batch_size,1)) # Assign a label of 1 to all real images.
    
    #Create empty lists to populate gen and disc losses. 
    g_losses = []
    d_losses = []
    
    #Enumerate training over batches. 
    for b in tqdm(range(len(train_hr_batches))):
        lr_imgs = train_lr_batches[b] #Fetch a batch of LR images for training
        hr_imgs = train_hr_batches[b] #Fetch a batch of HR images for training
        
        fake_imgs = generator.predict_on_batch(lr_imgs) #Fake images
        
        #First, train the discriminator on fake and real HR images. 
        discriminator.trainable = True
        d_loss_gen = discriminator.train_on_batch(fake_imgs, fake_label)
        d_loss_real = discriminator.train_on_batch(hr_imgs, real_label)
        
        #Now, train the generator by fixing discriminator as non-trainable
        discriminator.trainable = False
        
        #Average the discriminator loss, just for reporting purposes. 
        d_loss = 0.5 * np.add(d_loss_gen, d_loss_real) 
        
        #Extract VGG features, to be used towards calculating loss
        image_features = vgg.predict(hr_imgs)
     
        #Train the generator via GAN. 
        #Remember that we have 2 losses, adversarial loss and content (VGG) loss
        g_loss, _, _ = gan_model.train_on_batch([lr_imgs, hr_imgs], [real_label, image_features])
        
        #Save losses to a list so we can average and report. 
        d_losses.append(d_loss)
        g_losses.append(g_loss)
        
    #Convert the list of losses to an array to make it easy to average    
    g_losses = np.array(g_losses)
    d_losses = np.array(d_losses)
    
    #Calculate the average losses for generator and discriminator
    g_loss = np.sum(g_losses, axis=0) / len(g_losses)
    d_loss = np.sum(d_losses, axis=0) / len(d_losses)
    
    #Report the progress during training. 
    print("epoch:", e+1 ,"g_loss:", g_loss, "d_loss:", d_loss)

  0%|                                                                                           | 0/67 [00:00<?, ?it/s]

WARNING:tensorflow:5 out of the last 6703 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000272835B49D0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
1/1 [==============================] - 0s 12ms/step

  1%|█▏                                                                                 | 1/67 [00:00<00:57,  1.15it/s]

1/1 [==============================] - 0s 12ms/step

  3%|██▍                                                                                | 2/67 [00:01<00:29,  2.18it/s]

1/1 [==============================] - 0s 13ms/step

  4%|███▋                                                                               | 3/67 [00:01<00:20,  3.07it/s]

1/1 [==============================] - 0s 13ms/step

  6%|████▉                                                                              | 4/67 [00:01<00:16,  3.83it/s]

1/1 [==============================] - 0s 12ms/step


  if (e+1) % 10 == 0: #Change the frequency for model saving, if needed
        #Save the generator after every n epochs (Usually 10 epochs)
        generator.save("HR1/gen_e_"+ str(e+1) +".h5")

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.


from keras.models import load_model
from numpy.random import randint

generator = load_model('HR1/gen_e_200.h5', compile=False)
[X1, X2] = [lr_test, hr_test]
# select random example
ix = randint(0, len(X1), 1)
src_image, tar_image = X1[ix], X2[ix]

# generate image from source
gen_image = generator.predict(src_image)

1/1 [==============================] - 1s 734ms/step


plt.figure(figsize=(16, 8))
plt.subplot(231)
plt.title('LR Image')
plt.imshow(src_image[0,:,:,:])
plt.subplot(232)
plt.title('Superresolution')
plt.imshow(gen_image[0,:,:,:])
plt.subplot(233)
plt.title('Orig. HR image')
plt.imshow(tar_image[0,:,:,:])

plt.show()

Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).


# import os
import cv2
import numpy as np
from skimage.metrics import peak_signal_noise_ratio, structural_similarity

def evaluate_dataset(dataset_dir, sr_dir, resize_shape=(128,128)):
    psnr_values = []
    ssim_values = []
    mse_values = []

    for filename in os.listdir(dataset_dir):
        if filename.endswith('.png') or filename.endswith('.jpg'):
            hr_image = cv2.imread(os.path.join(dataset_dir, filename))
            sr_image = cv2.imread(os.path.join(sr_dir, filename))
            hr_image = cv2.cvtColor(hr_image, cv2.COLOR_BGR2RGB)
            sr_image = cv2.cvtColor(sr_image, cv2.COLOR_BGR2RGB)

            hr_image = cv2.resize(hr_image, resize_shape)
            sr_image = cv2.resize(sr_image, resize_shape)

            psnr = peak_signal_noise_ratio(hr_image, sr_image)
            ssim = structural_similarity(hr_image, sr_image, multichannel=True)
            mse = np.mean((hr_image - sr_image) ** 2)

            psnr_values.append(psnr)
            ssim_values.append(ssim)
            mse_values.append(mse)

    avg_psnr = np.mean(psnr_values)
    avg_ssim = np.mean(ssim_values)
    avg_mse = np.mean(mse_values)

    print(f'Average PSNR: {avg_psnr:.2f}')
    print(f'Average SSIM: {avg_ssim:.2f}')
    print(f'Average MSE: {avg_mse:.2f}')

# Example usage
evaluate_dataset('HR1/lr1', 'HR1/HR', resize_shape=(128,128))

C:\Users\admin\AppData\Local\Temp\ipykernel_14976\3834729049.py:22: FutureWarning: `multichannel` is a deprecated argument name for `structural_similarity`. It will be removed in version 1.0. Please use `channel_axis` instead.
  ssim = structural_similarity(hr_image, sr_image, multichannel=True)

Average PSNR: 21.19
Average SSIM: 0.62
Average MSE: 65.59


import os
import cv2
import numpy as np
from skimage.metrics import peak_signal_noise_ratio, mean_squared_error, structural_similarity

def evaluate_image(hr_image, sr_image):
    hr_image = cv2.cvtColor(hr_image, cv2.COLOR_BGR2RGB)
    sr_image = cv2.cvtColor(sr_image, cv2.COLOR_BGR2RGB)

    psnr = peak_signal_noise_ratio(hr_image, sr_image)
    mse = np.mean((hr_image - sr_image) ** 2)
    ssim = structural_similarity(hr_image, sr_image, multichannel=True)

    return psnr, mse, ssim

def display_images_with_metrics(dataset_dir, sr_dir):
    for filename in os.listdir(dataset_dir):
        if filename.endswith('.png') or filename.endswith('.jpg'):
            hr_image = cv2.imread(os.path.join(dataset_dir, filename))
            # Resize the high-resolution image to match the dimensions of the super-resolution image
            sr_image = cv2.imread(os.path.join(sr_dir, filename))
            hr_image = cv2.resize(hr_image, (sr_image.shape[1], sr_image.shape[0]))
            
            psnr, mse, ssim = evaluate_image(hr_image, sr_image)

            print(f'Filename: {filename}, PSNR: {psnr:.2f}, MSE: {mse:.2f}, SSIM: {ssim:.2f}')

            cv2.imshow('HR Image', hr_image)
            cv2.imshow('SR Image', sr_image)
            cv2.waitKey(0)
            cv2.destroyAllWindows()

# Example usage
display_images_with_metrics('HR1/lr1', 'HR1/HR')

C:\Users\admin\AppData\Local\Temp\ipykernel_14976\478753859.py:12: FutureWarning: `multichannel` is a deprecated argument name for `structural_similarity`. It will be removed in version 1.0. Please use `channel_axis` instead.
  ssim = structural_similarity(hr_image, sr_image, multichannel=True)

Filename: 0.png, PSNR: 20.38, MSE: 85.25, SSIM: 0.52
Filename: 1.png, PSNR: 25.03, MSE: 19.20, SSIM: 0.91
Filename: 10.png, PSNR: 24.09, MSE: 62.28, SSIM: 0.61
Filename: 11.png, PSNR: 19.39, MSE: 69.02, SSIM: 0.63
Filename: 12.png, PSNR: 20.06, MSE: 68.95, SSIM: 0.66
Filename: 13.png, PSNR: 24.53, MSE: 31.94, SSIM: 0.79
Filename: 14.png, PSNR: 20.80, MSE: 80.48, SSIM: 0.56
Filename: 15.png, PSNR: 23.43, MSE: 62.06, SSIM: 0.65
Filename: 16.png, PSNR: 20.84, MSE: 66.75, SSIM: 0.59
Filename: 17.png, PSNR: 19.85, MSE: 63.41, SSIM: 0.64
Filename: 18.png, PSNR: 20.84, MSE: 78.85, SSIM: 0.56
Filename: 19.png, PSNR: 18.41, MSE: 87.11, SSIM: 0.47
Filename: 2.png, PSNR: 22.06, MSE: 61.57, SSIM: 0.73
Filename: 20.png, PSNR: 15.21, MSE: 100.95, SSIM: 0.43
Filename: 21.png, PSNR: 21.35, MSE: 84.12, SSIM: 0.49
Filename: 22.png, PSNR: 21.01, MSE: 54.27, SSIM: 0.68
Filename: 23.png, PSNR: 18.43, MSE: 67.94, SSIM: 0.56
Filename: 24.png, PSNR: 17.53, MSE: 86.68, SSIM: 0.46
Filename: 25.png, PSNR: 20.44, MSE: 60.33, SSIM: 0.68
Filename: 26.png, PSNR: 26.74, MSE: 36.91, SSIM: 0.72
Filename: 27.png, PSNR: 21.56, MSE: 73.25, SSIM: 0.46
Filename: 28.png, PSNR: 21.27, MSE: 60.14, SSIM: 0.63
Filename: 29.png, PSNR: 23.32, MSE: 50.70, SSIM: 0.69
Filename: 3.png, PSNR: 18.44, MSE: 85.12, SSIM: 0.57
Filename: 30.png, PSNR: 19.57, MSE: 80.42, SSIM: 0.56
Filename: 31.png, PSNR: 18.86, MSE: 76.14, SSIM: 0.57
Filename: 32.png, PSNR: 20.83, MSE: 48.22, SSIM: 0.75
Filename: 33.png, PSNR: 20.93, MSE: 59.76, SSIM: 0.67
Filename: 34.png, PSNR: 21.89, MSE: 76.80, SSIM: 0.60
Filename: 35.png, PSNR: 19.56, MSE: 70.01, SSIM: 0.59
Filename: 36.png, PSNR: 18.44, MSE: 91.07, SSIM: 0.46
Filename: 37.png, PSNR: 23.02, MSE: 55.84, SSIM: 0.76
Filename: 38.png, PSNR: 22.89, MSE: 51.90, SSIM: 0.72
Filename: 39.png, PSNR: 20.42, MSE: 87.01, SSIM: 0.55
Filename: 4.png, PSNR: 21.96, MSE: 76.12, SSIM: 0.65
Filename: 40.png, PSNR: 22.51, MSE: 58.52, SSIM: 0.64
Filename: 41.png, PSNR: 21.06, MSE: 63.77, SSIM: 0.68
Filename: 42.png, PSNR: 24.02, MSE: 20.90, SSIM: 0.89
Filename: 43.png, PSNR: 24.09, MSE: 30.10, SSIM: 0.80
Filename: 44.png, PSNR: 19.96, MSE: 60.65, SSIM: 0.62
Filename: 45.png, PSNR: 20.53, MSE: 64.74, SSIM: 0.56
Filename: 46.png, PSNR: 18.38, MSE: 76.60, SSIM: 0.61
Filename: 47.png, PSNR: 18.22, MSE: 79.72, SSIM: 0.61
Filename: 48.png, PSNR: 15.99, MSE: 96.93, SSIM: 0.43
Filename: 49.png, PSNR: 21.29, MSE: 68.71, SSIM: 0.58
Filename: 5.png, PSNR: 22.43, MSE: 59.08, SSIM: 0.72
Filename: 50.png, PSNR: 20.53, MSE: 48.69, SSIM: 0.69
Filename: 51.png, PSNR: 28.93, MSE: 32.31, SSIM: 0.75
Filename: 52.png, PSNR: 23.18, MSE: 38.12, SSIM: 0.77
Filename: 53.png, PSNR: 20.44, MSE: 67.61, SSIM: 0.58
Filename: 54.png, PSNR: 24.04, MSE: 55.14, SSIM: 0.73
Filename: 55.png, PSNR: 24.61, MSE: 59.54, SSIM: 0.55
Filename: 56.png, PSNR: 25.56, MSE: 45.08, SSIM: 0.80
Filename: 57.png, PSNR: 23.16, MSE: 62.03, SSIM: 0.58
Filename: 58.png, PSNR: 21.52, MSE: 75.48, SSIM: 0.57
Filename: 59.png, PSNR: 19.37, MSE: 89.32, SSIM: 0.48
Filename: 6.png, PSNR: 21.15, MSE: 73.60, SSIM: 0.57
Filename: 60.png, PSNR: 20.29, MSE: 65.83, SSIM: 0.58
Filename: 61.png, PSNR: 28.80, MSE: 29.68, SSIM: 0.84
Filename: 62.png, PSNR: 21.83, MSE: 57.69, SSIM: 0.62
Filename: 63.png, PSNR: 22.88, MSE: 77.76, SSIM: 0.59
Filename: 64.png, PSNR: 21.99, MSE: 67.28, SSIM: 0.59
Filename: 65.png, PSNR: 19.20, MSE: 85.72, SSIM: 0.51
Filename: 66.png, PSNR: 18.99, MSE: 88.03, SSIM: 0.47
Filename: 67.png, PSNR: 23.70, MSE: 22.07, SSIM: 0.86
Filename: 68.png, PSNR: 21.96, MSE: 58.95, SSIM: 0.65
Filename: 69.png, PSNR: 20.50, MSE: 87.86, SSIM: 0.45
Filename: 7.png, PSNR: 21.87, MSE: 40.51, SSIM: 0.77
Filename: 70.png, PSNR: 20.11, MSE: 81.18, SSIM: 0.59
Filename: 71.png, PSNR: 18.51, MSE: 86.53, SSIM: 0.58
Filename: 72.png, PSNR: 22.04, MSE: 68.69, SSIM: 0.57
Filename: 73.png, PSNR: 22.58, MSE: 61.18, SSIM: 0.59
Filename: 74.png, PSNR: 17.51, MSE: 95.71, SSIM: 0.43
Filename: 75.png, PSNR: 22.66, MSE: 72.30, SSIM: 0.55
Filename: 76.png, PSNR: 23.09, MSE: 47.32, SSIM: 0.63
Filename: 77.png, PSNR: 20.57, MSE: 80.07, SSIM: 0.56
Filename: 78.png, PSNR: 19.98, MSE: 50.21, SSIM: 0.69
Filename: 79.png, PSNR: 23.47, MSE: 41.43, SSIM: 0.74
Filename: 8.png, PSNR: 23.20, MSE: 71.89, SSIM: 0.58
Filename: 80.png, PSNR: 23.87, MSE: 47.77, SSIM: 0.72
Filename: 81.png, PSNR: 17.93, MSE: 79.31, SSIM: 0.58
Filename: 82.png, PSNR: 19.50, MSE: 81.44, SSIM: 0.55
Filename: 83.png, PSNR: 19.65, MSE: 85.59, SSIM: 0.47
Filename: 84.png, PSNR: 22.80, MSE: 65.00, SSIM: 0.57
Filename: 85.png, PSNR: 22.16, MSE: 49.88, SSIM: 0.76
Filename: 86.png, PSNR: 21.24, MSE: 54.59, SSIM: 0.64
Filename: 87.png, PSNR: 17.26, MSE: 89.59, SSIM: 0.55
Filename: 88.png, PSNR: 17.41, MSE: 94.29, SSIM: 0.46
Filename: 89.png, PSNR: 24.67, MSE: 46.83, SSIM: 0.73
Filename: 9.png, PSNR: 24.74, MSE: 55.83, SSIM: 0.70
Filename: 90.png, PSNR: 18.34, MSE: 88.92, SSIM: 0.53
Filename: 91.png, PSNR: 19.25, MSE: 91.61, SSIM: 0.30
Filename: 92.png, PSNR: 20.16, MSE: 61.73, SSIM: 0.58
Filename: 93.png, PSNR: 22.06, MSE: 49.39, SSIM: 0.69
Filename: 94.png, PSNR: 21.20, MSE: 76.71, SSIM: 0.54
Filename: 95.png, PSNR: 20.41, MSE: 33.46, SSIM: 0.82
Filename: 96.png, PSNR: 16.67, MSE: 102.49, SSIM: 0.27
Filename: 97.png, PSNR: 22.46, MSE: 28.21, SSIM: 0.82
Filename: 98.png, PSNR: 17.66, MSE: 74.62, SSIM: 0.44
Filename: 99.png, PSNR: 19.04, MSE: 66.95, SSIM: 0.56


#unsharp masking
#import libraries
from PIL import Image
import numpy as np
import cv2
import math
#read image
img = cv2.imread('HR1/HR/78.png') 
#blur the image using gaussian blur
gaussian = cv2.GaussianBlur(img, (7, 7),0)
#store images as arrays
img=np.asarray(img)
gaussian=np.asarray(gaussian)
#subtract blurred image from original, then add to original
img_unsharp=img+(img-gaussian)
#output unsharp image
cv2.imwrite('output/saved_img.png', img_unsharp)
#convert array to image
img_unsharp=Image.fromarray(img_unsharp)
img_unsharp.show()


#contrast stretching
from PIL import Image

# Method to process the red band of the image
def normalizeRed(intensity):
    iI      = intensity
    minI    = 86
    maxI    = 230
    minO    = 0
    maxO    = 255
    iO      = (iI-minI)*(((maxO-minO)/(maxI-minI))+minO)
    return iO
# Method to process the green band of the image
def normalizeGreen(intensity):
    iI      = intensity
    minI    = 90
    maxI    = 225
    minO    = 0
    maxO    = 255
    iO      = (iI-minI)*(((maxO-minO)/(maxI-minI))+minO)
    return iO

# Method to process the blue band of the image
def normalizeBlue(intensity):
    iI      = intensity   
    minI    = 100
    maxI    = 210
    minO    = 0
    maxO    = 255
    iO      = (iI-minI)*(((maxO-minO)/(maxI-minI))+minO)
    return iO

#Create an image object
imageObject     = Image.open("HR1/HR/78.png")
# Split the red, green and blue bands from the Image
multiBands      = imageObject.split()
# Apply point operations that does contrast stretching on each color band
normalizedRedBand      = multiBands[0].point(normalizeRed)
normalizedGreenBand    = multiBands[1].point(normalizeGreen)
normalizedBlueBand     = multiBands[2].point(normalizeBlue)
# Create a new image from the contrast stretched red, green and blue brands
normalizedImage = Image.merge("RGB", (normalizedRedBand, normalizedGreenBand, normalizedBlueBand))
# Display the image before contrast stretching
imageObject.show()


# Display the image after contrast stretching
normalizedImage.show()